Mastering pandas for finance : master pandas, an open source Python data analysis library, for financial data analysis /

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If you are interested in quantitative finance, financial modeling, and trading, or simply want to learn how Python and pandas can be applied to finance, then this book is ideal for you. Some knowledge of Python and pandas is assumed. Interest in financial concepts is helpful, but no prior knowledge...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Heydt, Michael (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing, 2015.
Colección:Community experience distilled.
Temas:
Python (Computer program language)
Finance.
Quantitative research.
Open source software.
Python (Langage de programmation)
Finances.
Recherche quantitative.
Logiciels libres.
finance.
BUSINESS & ECONOMICS > Finance.
Finance
Open source software
Quantitative research
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • ""Cover""; ""Copyright""; ""Credits""; ""About the Author""; ""About the Reviewers""; ""www.PacktPub.com""; ""Table of Contents""; ""Preface""; ""Chapter 1: Getting Started with pandas Using Wakari.io""; ""What is Wakari?""; ""Creating a Wakari cloud account""; ""Updating existing packages""; ""Installing new packages""; ""Installing the samples in Wakari""; ""Summary""; ""Chapter 2: Introducing the Series and DataFrame""; ""Notebook setup""; ""The main pandas data structures â€? Series and DataFrame""; ""The Series""; ""The DataFrame""; ""The basics of the Series and DataFrame objects""
  • ""Creating a Series and accessing elements""""Size, shape, uniqueness, and counts of values""; ""Alignment via index labels""; ""Creating a DataFrame""; ""Example data""; ""Selecting columns of a DataFrame""; ""Selecting rows of a DataFrame using the index""; ""Slicing using the [] operator""; ""Selecting rows by the index label and location â€? .loc[] and .iloc[]""; ""Selecting rows by the index label and/or location â€? .ix[]""; ""Scalar lookup by label or location using .at[] and .iat[]""; ""Selecting rows using the Boolean selection""; ""Arithmetic on a DataFrame""
  • Reindexing the Series and DataFrame objectsSummary
  • Chapter 3: Reshaping, Reorganizing, and Aggregating
  • Notebook setup
  • Loading historical stock data
  • Organizing the data for the examples
  • Reorganizing and reshaping data
  • Concatenating multiple DataFrame objects
  • Merging DataFrame objects
  • Pivoting
  • Stacking and unstacking
  • Melting
  • Grouping and aggregating
  • Splitting
  • Aggregating
  • Summary
  • Chapter 4: Time-series
  • Notebook setup
  • Time-series data and the DatetimeIndex
  • Creating time-series with specific frequenciesRepresenting intervals of time using periods
  • Shifting and lagging time-series data
  • Frequency conversion of time-series data
  • Resampling of time-series
  • Summary
  • Chapter 5: Time-series Stock Data
  • Notebook setup
  • Obtaining historical stock and index data
  • Fetching historical stock data from Yahoo!
  • Fetching index data from Yahoo!
  • Visualizing financial time-series data
  • Plotting closing prices
  • Plotting volume-series data
  • Combined price and volumes
  • Plotting candlesticks
  • Fundamental financial calculationsCalculating simple daily percentage change
  • Calculating simple daily cumulative returns
  • Analyzing the distribution of returns
  • Histograms
  • Q-Q plots
  • Box-and-whisker plots
  • Comparison of daily percentage change between stocks
  • Moving windows
  • Volatility calculation
  • Rolling correlation of returns
  • Least-squares regression of returns
  • Comparing stocks to the S & P 500
  • Summary
  • Chapter 6: Trading Using Google Trends
  • Notebook setup

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